--- language: - pt license: cc0-1.0 task_categories: - text-classification - token-classification size_categories: - 100K= 0.80. **Tweets:** 90,822 unique tweet IDs. **Authors:** 25,483 anonymized Community Notes participant IDs. **Entities:** 510,352 post-processed entity rows, with 21 observed canonical entity types. **Topics:** 898 valid fine-grained topics, plus BERTopic outliers (`topic_id = -1`), organized into 38 macrothemes. **Syntax layer:** 292,275 automatic UD sentences and 5,599,679 token rows. **Current documentation state:** updated after the entity post-processing and quality-audit rounds of June 2026 and the publication of the associated SciELO preprint in August 2026. ## Resumo em português Este é um subconjunto curado em PT-BR das Community Notes da X, enriquecido para pesquisa em PLN, mineração de texto e moderação colaborativa. Ele inclui 142.448 notas, 90.822 tweets distintos, 510.352 entidades nomeadas pós-processadas, 898 tópicos válidos, 38 macrotemas, campos de pontuação por fatoração matricial e uma camada opcional de sintaxe Universal Dependencies. A versão atual também publica tabelas de auditoria em `quality_audit/`, criadas para tornar explícitos os problemas e limites de offsets de entidades, alinhamento com UD e integridade entre notas, tópicos e entidades. As anotações são automáticas e não passaram por revisão humana linha a linha. As tabelas de auditoria devem ser usadas como parte do dataset, especialmente quando a análise depende de spans, offsets ou alinhamento entre entidades e sentenças. --- ## Files | File or folder | Rows | Size | Granularity / role | |---|---:|---:|---| | `notes_pt.parquet` | 142,448 | 42.4 MB | one row per Portuguese Community Note | | `entities.parquet` | 510,352 | 68.8 MB | one row per extracted entity | | `topic_metadata.parquet` | 898 | 78.3 KB | one row per valid BERTopic cluster | | `macrotheme_metadata.parquet` | 38 | 9.4 KB | one row per macrotheme | | `DATA_DICTIONARY.md` | - | - | explicit data dictionary with column-level descriptions | | `taxonomy.json` | - | 5.0 KB | entity extraction taxonomy and thresholds | | `syntax_ud_bosque/sentences/*.parquet` | 292,275 | 22.8 MB | automatic UD sentence spans | | `syntax_ud_bosque/tokens/*.parquet` | 5,599,679 | 89.7 MB | automatic UD token-level annotations | | `syntax_ud_bosque/notes_udpipe_bosque.conllu.zst` | - | 42.3 MB | compressed CoNLL-U export | | `quality_audit/` | - | 90.7 MB | automatic validation tables | | `cross_modal_validation/` | - | 64.6 KB | macrotheme/entity/domain consistency previews | | `source_code/` | - | 2.3 MB | sanitized construction and analysis notebooks | Primary join key: `noteId` between `notes_pt.parquet`, `entities.parquet`, the UD files, and the audit tables. `tweetId` is kept for tweet hydration and cross-reference, but tweet text is not included. The Dataset Viewer is intentionally limited to the main analysis tables and the optional UD syntax layer. The automatic quality-audit tables remain available under `quality_audit/` in the repository files. The core dataset is released under CC0. The optional `syntax_ud_bosque/` layer was generated with UDPipe using the `portuguese-bosque-ud-2.17-251125` model, whose license is reported by the API as `CC BY-NC-SA`; see `syntax_ud_bosque/udpipe_bosque_metadata.json`. --- ## Schema For a complete column-level data dictionary, including keys, Parquet types, controlled values and interpretation notes, see [`DATA_DICTIONARY.md`](DATA_DICTIONARY.md). The summary below highlights the main analysis tables. ### `notes_pt.parquet` | Column | Description | |---|---| | `noteId` | Primary key, the official Community Notes note identifier. | | `tweetId` | The tweet the note refers to. | | `note_author_participant_id` | Anonymized Community Notes participant ID. | | `created_at`, `year_month` | Note creation time in UTC and monthly convenience key. | | `summary` | Note text as published in the Community Notes data. | | `summary_clean` | Whitespace-normalized and URL-stripped text used by embeddings and UD parsing. | | `classification_algoritmo` | Original Community Notes note classification, such as `MISINFORMED_OR_POTENTIALLY_MISLEADING` or `NOT_MISLEADING`. | | `idioma`, `confianca_idioma` | fastText language label and confidence. All retained rows have `idioma = pt` and confidence >= 0.80. | | `status_final`, `consenso` | Latest Community Notes status and compact status class (`CRH`, `CRNH`, `NMR`, `Outro`). | | `n_ratings`, `n_notas_no_tweet` | Rating count for the note and number of notes attached to the same tweet. | | `coreNoteIntercept`, `coreNoteFactor1` | Outputs from the official Matrix Factorization scoring code, restricted to the PT subset. Null when a note has insufficient ratings for scoring. | | `topic_id`, `topic_name`, `contexto_central`, `categoria_ampla` | Fine-grained topic ID and LLM-generated topic metadata. `topic_id = -1` denotes BERTopic outliers. | | `macrotheme_id`, `macrotheme_label` | Hierarchical macrotheme ID and LLM-generated label. | | `stratum` | Hydration sample stratum (`top_notas`, `bat_signals`, `aleatorio`); null for unsampled notes. | ### `entities.parquet` | Column | Description | |---|---| | `entity_id` | Unique ID of the extraction, formatted as `_`. No duplicates are present in the current release. | | `noteId`, `tweetId` | Foreign keys to the note and tweet. | | `texto_entidade` | Surface form extracted from the note/extraction context. | | `texto_canonico` | Normalized form after lowercasing, unidecode and dictionary-based canonicalization where applicable. | | `tipo_entidade` | Canonical Portuguese entity type. The current table has 21 observed types; `taxonomy.json` documents extraction rules and regex aliases. | | `label_modelo` | Original GLiNER label or regex extractor name. | | `inicio`, `fim` | Generated character offsets from extraction. Treat them as automatic spans, not as gold offsets against `summary_clean`; use `quality_audit/entity_offset_audit.parquet` before span-level analysis. | | `score` | GLiNER confidence or high-confidence regex score. | | `fonte_extracao` | Extraction source: GLiNER or one of the regex extractors for URLs, dates, percentages, monetary values, legal references or court-process numbers. | | `contexto` | Local context window around the entity. | | `papel_no_texto` | `mencao` or `fonte_ou_evidencia`. | | `dominio` | Canonicalized domain for URL/domain entities. | | `topic_id`, `macrotheme_id` | Denormalized topic and macrotheme IDs copied from the source note for analysis convenience. | ### `syntax_ud_bosque/` The optional syntax layer is automatic UDPipe output over `notes_pt.summary_clean`. | Folder | Description | |---|---| | `sentences/part-*.parquet` | Sentence-level spans, text and sentence IDs. | | `tokens/part-*.parquet` | Token-level CoNLL-U fields plus `noteId` and sentence metadata. | | `notes_udpipe_bosque.conllu.zst` | Compressed CoNLL-U export with note-level comments. | ### `quality_audit/` | File | Rows | Description | |---|---:|---| | `entity_offset_audit.parquet` | 510,352 | Checks whether `texto_entidade` and offsets validate against `notes_pt.summary_clean`. | | `ud_sentence_tokenization_audit.parquet` | 292,275 | Checks whether UD sentence text can be located in `summary_clean`, and whether token counts and token IDs are coherent. | | `entity_ud_alignment.parquet` | 510,352 | Aligns entities with UD sentence spans when offsets validate. | | `notes_topic_integrity_audit.parquet` | 142,448 | Validates note-level topic and macrotheme references. | | `entities_topic_integrity_audit.parquet` | 510,352 | Checks whether entity topic/macrotheme IDs match their source note. | | `quality_audit_summary.parquet` / `.csv` | 27 | Compact count table for all audit statuses. | --- ## Construction Methodology ### 1. Community Notes ETL The five official Community Notes tables (`notes`, `noteRatings`, `noteStatusHistory`, `userEnrollment`, `batSignals`) were downloaded by snapshot date, hash-verified, checked for schema drift across shards and materialized in DuckDB. Bronze tables preserve the source fields as strings, silver tables add typed columns and snake_case names, and gold tables export compressed Parquet files. ### 2. Portuguese filter Each note's `summary_clean` was classified with fastText `lid.176.bin`. Notes with `idioma = pt` and `confianca_idioma >= 0.80` were retained, yielding 142,448 notes. A 300-note stratified sample across confidence bands was visually inspected and looked correct, but this was a quick sanity check rather than a formal human evaluation. ### 3. Topic and macrotheme modeling Notes were embedded with `intfloat/multilingual-e5-large-instruct` using a Portuguese instruction prompt oriented toward the factual matter under dispute. BERTopic was fitted with UMAP and HDBSCAN parameters selected by a DBCV grid search. Outliers were reduced conservatively by embedding similarity. Each valid topic was labeled by `Marco-Mini-Instruct` with grammar-constrained JSON output. Macrothemes were obtained by hierarchical aggregation over topic embeddings and labeled in a second LLM pass using topic descriptions rather than only keywords. A procedural-content filter was used to separate substantive topics from notes about Community Notes mechanics, satire without a target, and similar meta-platform content. ### 4. Named entity recognition Entity extraction combines: - GLiNER (`urchade/gliner_multi-v2.1`) with a Portuguese taxonomy for people, political actors, parties, public bodies, courts, security forces, media outlets, platforms, public programs, organizations, locations and political events. - Regex extractors for structured patterns such as domains/URLs, dates, percentages, monetary values, legal references and judicial process numbers. - Canonical dictionaries for public programs, public agencies, parties, platforms and recurring media/domain variants. - Overlap resolution favoring higher-precision regex outputs and canonical types. The current release includes automatic post-processing after the first extraction. It removed generic role nouns and extraction markers, corrected known media/platform/party type conflicts, applied a POS/UD-assisted filter to single-token false positives, and refined the separation among `PESSOA`, `PESSOA_PUBLICA` and `ATOR_POLITICO`. The post-processing was conservative and automatic; it was not a human line-by-line adjudication. Post-processing history for `entities.parquet`: | Stage | Rows | Notes | |---|---:|---| | Initial extraction | 536,373 | GLiNER + regex + canonicalization. | | Structural/entity cleanup | 521,992 | Removed extraction markers, malformed standalone URL tokens, generic role nouns and a few systematic type conflicts. | | POS/UD-assisted cleanup | 510,352 | Removed 11,640 single-token false positives using morphology/POS and lexicon rules. | | Person-type refinement | 510,352 | Reclassified 1,282 public/personal names as `ATOR_POLITICO` and 126 non-political public figures away from `ATOR_POLITICO`. | Current `entities.parquet` SHA-256: `6c4f87b6d41d839b610043668d47c5ba95b7a3bf42ebb4b2ba786a0e5be53388`. ### 5. Matrix Factorization scoring The official Twitter/X `communitynotes` Matrix Factorization scoring code was run on the PT subset with minimal compatibility patches for the smaller note universe. The release includes `coreNoteIntercept` and `coreNoteFactor1`, restricted to Portuguese notes. These fields are null for notes with too few ratings. ### 6. Syntax and audit layers The UD syntax layer was generated automatically with UDPipe Bosque over `summary_clean`. The `quality_audit/` tables were added after the post-processing rounds to document offset validity, entity-to-UD alignment and topic/entity referential integrity. These tables are intended to make downstream filtering explicit instead of silently hiding uncertain spans. --- ## Source Code, Reproducibility and Exploration The `source_code/` folder contains sanitized notebooks used to construct and analyze the dataset: | Notebook | Purpose | |---|---| | `01_etl_snapshot.ipynb` | upstream snapshot ETL and typed analytical tables | | `02_portuguese_filter.ipynb` | Portuguese-language filtering and ratings/status joins | | `03_topic_modeling_macrothemes.ipynb` | embeddings, topic modeling, LLM labels and macrothemes | | `04_named_entity_recognition.ipynb` | GLiNER, regex extractors and canonical dictionaries | | `05_matrix_factorization_scoring.ipynb` | reproduction of Matrix Factorization scoring fields | | `06_helpfulness_classification.ipynb` | exploratory helpfulness/consensus classification experiments | | `07_udpipe_bosque_enrichment_colab.ipynb` | optional Colab pipeline for UDPipe Bosque annotations | Notebook outputs and embedded credentials were removed before publication. Full reproduction requires the upstream Community Notes data, local credentials where needed, optional X API access for hydration, and sufficient CPU/GPU resources for the modeling stages. The notebooks are transparent research code, not a one-command production package. Public exploration interfaces: - Static project catalog: https://communitynotesbr.netlify.app/ - Argument/entity explorer: https://explorador-argumentos.netlify.app/ --- ## Quantitative Description ### Volume by status | `consenso` | Source status | n_notes | % | |---|---|---:|---:| | `NMR` | `NEEDS_MORE_RATINGS` | 116,741 | 82.0 | | `CRH` | `CURRENTLY_RATED_HELPFUL` | 15,857 | 11.1 | | `CRNH` | `CURRENTLY_RATED_NOT_HELPFUL` | 4,423 | 3.1 | | `Outro` | null or unmapped | 5,427 | 3.8 | ### Topic coverage | Status | n_notes | % | |---|---:|---:| | Valid topic and macrotheme | 94,889 | 66.6 | | BERTopic outlier (`topic_id = -1`) | 47,162 | 33.1 | | Missing topic ID | 397 | 0.3 | ### Entities by type | `tipo_entidade` | n | |---|---:| | `URL_DOMINIO` | 174,624 | | `PESSOA` | 58,543 | | `LOCAL` | 49,022 | | `ORGANIZACAO` | 46,835 | | `VEICULO_MIDIA` | 43,292 | | `ATOR_POLITICO` | 33,617 | | `PLATAFORMA_DIGITAL` | 27,889 | | `ORGAO_PUBLICO` | 16,740 | | `DATA` | 13,292 | | `PARTIDO` | 9,016 | | `ESTATISTICA` | 7,519 | | `EVENTO_POLITICO` | 7,347 | | `PROGRAMA_PUBLICO` | 5,732 | | `VALOR_MONETARIO` | 4,224 | | `ORGAO_SEGURANCA` | 4,196 | | `ORGAO_JUDICIARIO` | 2,828 | | `LEI_NORMA` | 1,789 | | `OPERACAO_POLICIAL` | 1,170 | | `PESSOA_PUBLICA` | 1,159 | | `PROCESSO_JUDICIAL` | 888 | | `FONTE_CITADA` | 630 | 126,533 notes (88.83%) have at least one entity. The current release has a mean of 4.03 entities per annotated note and 3.58 entities per note overall. ### Missing data | Field family | Missingness | Interpretation | |---|---:|---| | `coreNoteIntercept` / `coreNoteFactor1` | 27.3% | Notes with too few ratings are not scored by the MF algorithm. | | `topic_id` | 397 rows (0.3%) | Notes outside the topic-model run. | | `topic_name`, `contexto_central`, `categoria_ampla` | 47,559 rows (33.4%) | BERTopic outliers plus missing topic IDs. | | `macrotheme_id`, `macrotheme_label` | 47,559 rows (33.4%) | Same scope as topic labels. | | `status_final` | 5,427 rows (3.8%) | Status empty or unmapped in the snapshot. | | `stratum` | 138,393 rows (97.2%) | Only 4,055 notes were selected for tweet-hydration sampling. | --- ## Quality Assessment No exhaustive human validation was performed. The release instead provides automatic checks and transparent failure-mode reporting. ### Deterministic checks The ETL uses file hashes, schema-drift checks, referential integrity checks and snapshot-to-snapshot comparisons. In the current release, `entities.entity_id` has zero duplicates and every entity row matches the topic and macrotheme IDs of its source note. ### Entity offset audit `quality_audit/entity_offset_audit.parquet` checks `entities.parquet` against `notes_pt.summary_clean`. | Audit status | n_entities | |---|---:| | `exact` | 238,770 | | `normalized_exact` | 45 | | `mismatch` | 11,384 | | `out_of_bounds` | 260,153 | Important interpretation: many offsets are not relative to `summary_clean`. Several URL/evidence spans appear to have been extracted from a richer extraction text where links were inserted into or appended to the note. The audit therefore flags these rows instead of rewriting offsets automatically. For span-level tasks, prefer rows with `exact` or `normalized_exact`, or inspect the shifted/repeated-text categories in the audit table. ### Entity-to-UD alignment | Alignment status | n_entities | |---|---:| | `aligned_to_ud_sentence` | 237,738 | | `entity_offset_not_validated_against_summary_clean` | 271,537 | | `entity_crosses_or_exceeds_sentence_span` | 626 | | `no_sentence_covering_offset` | 257 | | `no_valid_ud_sentence_span` | 194 | This alignment is useful for POS/morphology-aware filtering and syntactic analyses, but it should not be treated as a gold span annotation. ### UD sentence/token audit | Audit | Status | n_sentences | |---|---|---:| | Sentence location | `exact_in_order` | 292,149 | | Sentence location | `not_found` | 126 | | Token row count | `matches_n_conllu_rows` | 292,275 | | Token ID sequence | `sequential` | 292,275 | ### Topic integrity | Audit | Status | n | |---|---|---:| | Note topic reference | `topic_exists` | 94,889 | | Note topic reference | `no_topic_cluster` | 47,162 | | Note topic reference | `missing_topic_id` | 397 | | Entity topic reference | `topic_matches_note` | 510,352 | | Entity macrotheme reference | `macrotheme_matches_note` | 510,352 | ### Cross-modal validation The `cross_modal_validation/` folder contains compact tables comparing macrotheme labels with empirical entity-type and domain signatures. These are diagnostic previews rather than formal validation. They are useful for spotting residual procedural topics, overly broad macrothemes and labels whose entity/domain distribution is weakly concentrated. --- ## Limitations - **No human line-by-line adjudication.** Topic labels, entity types, offsets and UD parses are automatic. The post-processing rules are conservative but still rule-based. - **Tweet text is not redistributed.** This follows X terms and the Community Notes public-data design. Use `tweetId` for hydration when permitted. - **Snapshot-bound status.** Community Notes statuses evolve. The fields here reflect the 2026-04-07 snapshot. - **Portuguese filter tradeoff.** The fastText threshold favors precision and may exclude short, code-mixed or unusual Portuguese notes. - **Topic outliers.** 33.4% of notes have no valid topic/macrotheme label because they are BERTopic outliers or were outside the topic run. - **Entity offsets require care.** Offsets are not uniformly valid against `summary_clean`; consult `quality_audit/` for span-level work. - **Automatic syntax layer.** UD annotations may contain tokenization, lemmatization, POS-tagging, morphological or dependency errors, especially in informal text, sarcasm, named entities and short notes. - **NER taxonomy is domain-specific.** Types such as `ATOR_POLITICO`, `PESSOA_PUBLICA`, `ORGAO_PUBLICO` and `VEICULO_MIDIA` are designed for Brazilian Community Notes research and may not transfer directly to generic NER benchmarks. --- ## Suggested Use Cases - Portuguese NER evaluation and error analysis with politically salient entity categories. - Topic-conditioned studies of source citation, media domains and evidence practices. - Helpful-note classification using text, rating/status fields and MF scoring features. - Polarization analysis using `coreNoteFactor1`, topic labels and macrothemes. - Syntax-aware studies combining entities with automatic UD POS/morphology features. - Procedural-content detection and macrotheme-quality diagnostics. - Public-interest research on Brazilian online discourse across the 2021-2026 Community Notes window. --- ## Citation / Como citar If you use this dataset, please cite the associated preprint: > Community Notes no Brasil: consenso, polarização e aceitabilidade algorítmica. (2026). Em *SciELO Preprints*. https://doi.org/10.1590/SciELOPreprints.15996 ```bibtex @article{Machado_da_Rocha_2026, title = {Community Notes no Brasil: consenso, polarização e aceitabilidade algorítmica}, url = {https://doi.org/10.1590/SciELOPreprints.15996}, doi = {10.1590/SciELOPreprints.15996}, publisher = {FapUNIFESP (SciELO)}, author = {Machado da Rocha, Davi}, year = {2026}, month = aug } ``` The dataset itself can be cited as: ```bibtex @misc{communitynotesbr2026, title = {Community Notes BR: An NLP-Enriched Portuguese Dataset for Collaborative Misinformation Moderation}, author = {Rocha, Davi Machado da}, year = {2026}, howpublished = {Hugging Face dataset}, url = {https://huggingface.co/datasets/histlearn/notas-comunidade-ptbr}, note = {Dataset associated with the preprint Community Notes no Brasil: consenso, polarização e aceitabilidade algorítmica (SciELO Preprints, DOI: 10.1590/SciELOPreprints.15996)} } ``` ## License The original Community Notes public data is released by X under CC0. Derivative annotations in the core release are distributed under **CC0 1.0 Universal** to match the source license. Tweet text is not included. The optional UDPipe Bosque syntax layer should be interpreted together with the model/license metadata in `syntax_ud_bosque/udpipe_bosque_metadata.json`. ## AI Usage Disclosure This dataset includes outputs from machine-learning and generative-AI components as part of its construction: - `intfloat/multilingual-e5-large-instruct` for embedding generation. - `Marco-Mini-Instruct` for structured topic and macrotheme labels, constrained to parseable JSON. - `urchade/gliner_multi-v2.1` for named-entity extraction. - UDPipe Bosque for automatic tokenization, POS tagging, morphology, lemmatization and dependency parsing. Generative AI also assisted with parts of the prose in this datacard and the associated SciELO preprint. The main released annotations are exactly the pipeline outputs and post-processing rules documented above; they should be treated as automatic research annotations, not as human gold labels. --- **Maintainer:** Davi Machado da Rocha (USP). **Upstream source:** Community Notes public data, snapshot 2026-04-07, X Corp under CC0.